What are the functions of Data Mining?

Data mining is defined as the process of discovering patterns in data. The process must be automatic or (more usually) semiautomatic. The patterns discovered must be meaningful in that they lead some advantage, usually an economic one.

Data mining have two major functions: 

1. Classification:

• Classification maps data into predefined groups or classes. 
• It is often referred to as supervised learning because the classes are determined before examining the data. 
• Classification creates a function from training data. The training data consist of pairs of input objects, and desired output. The output of the function can be a continuous value, or can predict a class label of the input object. 
• The task of the classification is to predict the value of the function for any valid input object after having seen only a small number of training examples.

 2. Clustering:

• Clustering is similar to classification except that the groups are not predefined, but rather defined by the data alone. 
• Clustering is alternatively referred to as unsupervised learning or segmentation. 
• It can be thought of as partitioning or segmenting the data into groups that might or might not be disjointed. 
• The clustering is usually accomplished by determining the similarity among the data on predefined attributes. The most similar data are grouped into clusters.

What is Machine Learning? What are different types of Machine Learning?

Machine Learning:

Machine learning provides the technical basis of data mining. It is a branch of artificial intelligence, which concerns the construction and study of systems that can learn from data.

For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

Types of Machine Learning:

 
Supervised learning is basically a synonym of classification. The supervision in the learning comes from the labeled instances in the training data. 

Unsupervised learning is essentially a synonym of clustering. The learning process is unsupervised since the input instances are not class labeled. 

Semi-supervised learning is a class of machine learning technology that make use of both labeled and unlabelled instances when learning a model. 

Active learning is a machine learning approach that lets users play an active role in the learning process. An active learning approach can ask a user (e.g., a domain expert) to label an instance, which may be from a set of unlabelled instances.

What is Data Mining? What Kinds of Data can be Mined?

Data mining is also known as Knowledge Discovery from Data, or KDD for short, which turns a large collection of data into knowledge. Data mining is a multidisciplinary field including machine learning, artificial intelligence, pattern recognition, knowledge-based systems, high-performance computing, database technology, and data visualization. 

• Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. 
• Data mining is the process of finding hidden information and patterns in a huge database. 
• Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.

 

Data, Information, and Knowledge
Data: Data are any recorded facts, numbers, or text that can be processed by a computer - scientific data, medical data, demographic data, financial data, and marking data.

Information: The patterns, associations, or relationships among all this data can provide information.

Knowledge: Information can be converted into knowledge about historical patterns and future trends.

 

What Kinds of Data can be Mined?
The most basic forms of data for mining are come from: 

1. Database Data 
2. Data Warehouses 
3. Transactional Data

What is method overloading?

Method overloading is a polymorphism technique that allows us to have same method in different ways. Overloaded method can have different number of parameters, different parameter types, and different arrangement of parameters but with same name.

What is an interface? When to use interface over abstract class?

An interface contains definitions for a group of related functionalities that a class or a struct can implement.

Use interface over abstract class:

a. If the functionality you are creating will be useful across a wide range of disparate objects, use an interface.

b. If you are designing small, concise bits of functionality, use interfaces. If you are designing large functional units, use an abstract class.